CSPS: An Adaptive Pooling Method for Image Classification

Wang, Jinzhuo; Wang, Wenmin; Wang, Ronggang; Gao, Wen

doi:10.1109/tmm.2016.2544099

Cited by 25 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following, we set l = 4 in (9), which denotes a moderate number of layers; and we set γ = 0.4545 in (2) and 3, which is just the traditional option. At the end of this section, we will analyze the sensitivity of the parameters l in (9), and γ in (2) and (3).…”

Section: Experiments Settingsmentioning

confidence: 99%

“…From the above experiments, we can see that the choice was appropriate. Now we first fix γ in QCFs (2) or (3), then enumerate a small range of values around 4 and compute the corresponding average performance indexes for QCFs (2) and (3). The results for QCFs (2) and (3) with different l and γ = 0.4545 are reported in Tables 11 and 12, respectively.…”

Section: Sensitivity Of the Parameters L And γmentioning

confidence: 99%

“…High-quality images have found many applications in multimedia and computer vision applications [1,2]. However, due to the limitations of image acquisition technology, imaging environment, and other factors, it is sometimes very difficult to obtain high-quality images and it is almost impossible to obtain images with low illuminance all the time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Tang

Yan

et al. 2020

Symmetry

View full text Add to dashboard Cite

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

show abstract

Section: Experiments Settingsmentioning

confidence: 99%

Section: Sensitivity Of the Parameters L And γmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Tang

Yan

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…Some approaches adaptively learned regions that distinguishable for classification [29,30]. Similar to Jia et al, some works tried to design better spatial regions for pooling to reduce the effect of background noise with the goal of image classification [31,32] and object detection [33]. As traditional pooling operation adopt a fixed block size for each image, we propose a variable block size pooling scheme named RAP, which is configurable optimized on the basis of integrated distortion metrics and provide ability of preserving higher quality to crucial local areas.…”

Section: Adaptive Poolingmentioning

confidence: 99%

Learning based Facial Image Compression with semantic fidelity metric

Chen

2019

Neurocomputing

View full text Add to dashboard Cite

Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework's efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC's superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms.

show abstract

“…There are spatial pooling studies in the literature, which are specifically used for face image classification and object-based surveillance [1,8,18,23]. Class-Specific Pooling Shapes (CSPS) is a recently proposed method, which can learn compact geometric information within the input image [29]. Another study adopts a categoryspecific distribution matrix to determine how patches of an image are pooled together [17].…”

Section: Related Workmentioning

confidence: 99%

Regp: A New Pooling Algorithm for Deep Convolutional Neural Networks

Yıldırım¹,

Baloğlu²

2019

NNW

View full text Add to dashboard Cite

In this paper, we propose a new pooling method for deep convolutional neural networks. Previously introduced pooling methods either have very simple assumptions or they depend on stochastic events. Different from those methods, RegP pooling intensely investigates the input data. The main idea of this approach is finding the most distinguishing parts in regions of the input by investigating neighborhood regions to construct the pooled representation. RegP pooling improves the efficiency of the learning process, which is clearly visible in the experimental results. Further, the proposed pooling method outperformed other widely used hand-crafted pooling methods on several benchmark datasets.

show abstract

CSPS: An Adaptive Pooling Method for Image Classification

Cited by 25 publications

References 40 publications

Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Learning based Facial Image Compression with semantic fidelity metric

Regp: A New Pooling Algorithm for Deep Convolutional Neural Networks

Contact Info

Product

Resources

About